




from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean




def 		lowerCAmelCase_ (						lowerCamelCase ):

				# A local function to see if a dot lands in the circle.
				def is_in_circle(lowerCamelCase						,  lowerCamelCase ) -> bool:
								__magic_name__						:						Dict										=sqrt((x**2) + (y**2) )
								# Our circle has a radius of 1, so a distance
								# greater than 1 would land outside the circle.
								return distance_from_centre <= 1

				# The proportion of guesses that landed in the circle
				__magic_name__						:						Union[str, Any]										=mean(
				    int(is_in_circle(uniform(-1.0						,  1.0 )						,  uniform(-1.0						,  1.0 ) ) )
				    for _ in range(lowerCamelCase ) )
				# The ratio of the area for circle to square is pi/4.
				__magic_name__						:						List[Any]										=proportion * 4
				print(F"The estimated value of pi is {pi_estimate}" )
				print(F"The numpy value of pi is {pi}" )
				print(F"The total error is {abs(pi - pi_estimate )}" )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase = 0.0						,  lowerCamelCase = 1.0						,  ):
				return mean(
				    function_to_integrate(uniform(lowerCamelCase						,  lowerCamelCase ) ) for _ in range(lowerCamelCase ) ) * (max_value - min_value)




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase = 0.0						,  lowerCamelCase = 1.0 ):

				def identity_function(lowerCamelCase ) -> float:
								return x

				__magic_name__						:						Optional[int]										=area_under_curve_estimator(
				    lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
				__magic_name__						:						str										=(max_value * max_value - min_value * min_value) / 2

				print("""******************""" )
				print(F"Estimating area under y=x where x varies from {min_value} to {max_value}" )
				print(F"Estimated value is {estimated_value}" )
				print(F"Expected value is {expected_value}" )
				print(F"Total error is {abs(estimated_value - expected_value )}" )
				print("""******************""" )




def 		lowerCAmelCase_ (						lowerCamelCase ):

				def function_to_integrate(lowerCamelCase ) -> float:
								return sqrt(4.0 - x * x )

				__magic_name__						:						Dict										=area_under_curve_estimator(
				    lowerCamelCase						,  lowerCamelCase						,  0.0						,  2.0 )

				print("""******************""" )
				print("""Estimating pi using area_under_curve_estimator""" )
				print(F"Estimated value is {estimated_value}" )
				print(F"Expected value is {pi}" )
				print(F"Total error is {abs(estimated_value - pi )}" )
				print("""******************""" )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
_SCREAMING_SNAKE_CASE 		=      logging.getLogger()

_SCREAMING_SNAKE_CASE 		=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    os.makedirs(lowerCamelCase_   ,   exist_ok=lowerCamelCase_				)
    UpperCamelCase			  =      {"""source""": """What is love ?""", """target""": """life"""}
    UpperCamelCase			  =      {"""train""": 12, """val""": 2, """test""": 2}
    for split in ["train", "test", "val"]:
     for field in ["source", "target"]:
      UpperCamelCase			  =      """\n""".join([contents[field]] * n_lines[split]				)
      with open(os.path.join(lowerCamelCase_   ,   f"""{split}.{field}"""				)   ,   """w"""				) as f:
       f.write(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						str = "pytorch"				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_auto_remove_tmp_dir()
    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """output"""				)
    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """data"""				)
    self._create_dummy_data(data_dir=lowerCamelCase_				)

    UpperCamelCase			  =      f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

    if gpus > 0:
     testargs.append(f"""--gpus={gpus}"""				)
     if is_apex_available():
      testargs.append("""--fp16"""				)
    else:
     testargs.append("""--gpus=0"""				)
     testargs.append("""--distributed_backend=ddp_cpu"""				)
     testargs.append("""--num_processes=2"""				)

    UpperCamelCase			  =      [sys.executable, str(Path(finetune_rag.__file__				).resolve()				)] + testargs
    execute_subprocess_async(lowerCamelCase_   ,   env=self.get_env()				)

    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """metrics.json"""				)
    with open(lowerCamelCase_				) as f:
     UpperCamelCase			  =      json.load(lowerCamelCase_				)
    return result







   @require_torch_gpu
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_multi_gpu
   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=2				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_gpu
   @require_ray
   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1   ,   distributed_retriever="""ray"""				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_multi_gpu
   @require_ray
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1   ,   distributed_retriever="""ray"""				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)
